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|
%global _empty_manifest_terminate_build 0
Name: python-mlrose-hiive
Version: 2.2.4
Release: 1
Summary: MLROSe: Machine Learning, Randomized Optimization and Search (hiive extended remix)
License: BSD
URL: https://github.com/hiive/mlrose
Source0: https://mirrors.aliyun.com/pypi/web/packages/d3/58/3444853cdc3fb0b2b004f3e90b93f18f665216c0ad5bba554b15bef11a25/mlrose_hiive-2.2.4.tar.gz
BuildArch: noarch
%description
# mlrose: Machine Learning, Randomized Optimization and SEarch
mlrose is a Python package for applying some of the most common randomized optimization and search algorithms to a range of different optimization problems, over both discrete- and continuous-valued parameter spaces.
## Project Background
mlrose was initially developed to support students of Georgia Tech's OMSCS/OMSA offering of CS 7641: Machine Learning.
It includes implementations of all randomized optimization algorithms taught in this course, as well as functionality to apply these algorithms to integer-string optimization problems, such as N-Queens and the Knapsack problem; continuous-valued optimization problems, such as the neural network weight problem; and tour optimization problems, such as the Travelling Salesperson problem. It also has the flexibility to solve user-defined optimization problems.
At the time of development, there did not exist a single Python package that collected all of this functionality together in the one location.
## Main Features
#### *Randomized Optimization Algorithms*
- Implementations of: hill climbing, randomized hill climbing, simulated annealing, genetic algorithm and (discrete) MIMIC;
- Solve both maximization and minimization problems;
- Define the algorithm's initial state or start from a random state;
- Define your own simulated annealing decay schedule or use one of three pre-defined, customizable decay schedules: geometric decay, arithmetic decay or exponential decay.
#### *Problem Types*
- Solve discrete-value (bit-string and integer-string), continuous-value and tour optimization (travelling salesperson) problems;
- Define your own fitness function for optimization or use a pre-defined function.
- Pre-defined fitness functions exist for solving the: One Max, Flip Flop, Four Peaks, Six Peaks, Continuous Peaks, Knapsack, Travelling Salesperson, N-Queens and Max-K Color optimization problems.
#### *Machine Learning Weight Optimization*
- Optimize the weights of neural networks, linear regression models and logistic regression models using randomized hill climbing, simulated annealing, the genetic algorithm or gradient descent;
- Supports classification and regression neural networks.
## Installation
mlrose was written in Python 3 and requires NumPy, SciPy and Scikit-Learn (sklearn).
The latest version can be installed using `pip`:
```
pip install mlrose-hiive
```
Once it is installed, simply import it like so:
```python
import mlrose_hiive
```
## Documentation
The official mlrose documentation can be found [here](https://mlrose.readthedocs.io/).
A Jupyter notebook containing the examples used in the documentation is also available [here](https://github.com/gkhayes/mlrose/blob/master/tutorial_examples.ipynb).
## Licensing, Authors, Acknowledgements
mlrose was written by Genevieve Hayes and is distributed under the [3-Clause BSD license](https://github.com/gkhayes/mlrose/blob/master/LICENSE).
You can cite mlrose in research publications and reports as follows:
* Rollings, A. (2020). ***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python, hiive extended remix***. https://github.com/hiive/mlrose. Accessed: *day month year*.
Please also keep the original author's citation:
* Hayes, G. (2019). ***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python***. https://github.com/gkhayes/mlrose. Accessed: *day month year*.
You can cite this fork in a similar way, but please be sure to reference the original work.
Thanks to David S. Park for the MIMIC enhancements (from https://github.com/parkds/mlrose).
BibTeX entry:
```
@misc{Hayes19,
author = {Hayes, G},
title = {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python}},
year = 2019,
howpublished = {\url{https://github.com/gkhayes/mlrose}},
note = {Accessed: day month year}
}
@misc{Rollings20,
author = {Rollings, A.},
title = {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python, hiive extended remix}},
year = 2020,
howpublished = {\url{https://github.com/hiive/mlrose}},
note = {Accessed: day month year}
}
```
%package -n python3-mlrose-hiive
Summary: MLROSe: Machine Learning, Randomized Optimization and Search (hiive extended remix)
Provides: python-mlrose-hiive
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-mlrose-hiive
# mlrose: Machine Learning, Randomized Optimization and SEarch
mlrose is a Python package for applying some of the most common randomized optimization and search algorithms to a range of different optimization problems, over both discrete- and continuous-valued parameter spaces.
## Project Background
mlrose was initially developed to support students of Georgia Tech's OMSCS/OMSA offering of CS 7641: Machine Learning.
It includes implementations of all randomized optimization algorithms taught in this course, as well as functionality to apply these algorithms to integer-string optimization problems, such as N-Queens and the Knapsack problem; continuous-valued optimization problems, such as the neural network weight problem; and tour optimization problems, such as the Travelling Salesperson problem. It also has the flexibility to solve user-defined optimization problems.
At the time of development, there did not exist a single Python package that collected all of this functionality together in the one location.
## Main Features
#### *Randomized Optimization Algorithms*
- Implementations of: hill climbing, randomized hill climbing, simulated annealing, genetic algorithm and (discrete) MIMIC;
- Solve both maximization and minimization problems;
- Define the algorithm's initial state or start from a random state;
- Define your own simulated annealing decay schedule or use one of three pre-defined, customizable decay schedules: geometric decay, arithmetic decay or exponential decay.
#### *Problem Types*
- Solve discrete-value (bit-string and integer-string), continuous-value and tour optimization (travelling salesperson) problems;
- Define your own fitness function for optimization or use a pre-defined function.
- Pre-defined fitness functions exist for solving the: One Max, Flip Flop, Four Peaks, Six Peaks, Continuous Peaks, Knapsack, Travelling Salesperson, N-Queens and Max-K Color optimization problems.
#### *Machine Learning Weight Optimization*
- Optimize the weights of neural networks, linear regression models and logistic regression models using randomized hill climbing, simulated annealing, the genetic algorithm or gradient descent;
- Supports classification and regression neural networks.
## Installation
mlrose was written in Python 3 and requires NumPy, SciPy and Scikit-Learn (sklearn).
The latest version can be installed using `pip`:
```
pip install mlrose-hiive
```
Once it is installed, simply import it like so:
```python
import mlrose_hiive
```
## Documentation
The official mlrose documentation can be found [here](https://mlrose.readthedocs.io/).
A Jupyter notebook containing the examples used in the documentation is also available [here](https://github.com/gkhayes/mlrose/blob/master/tutorial_examples.ipynb).
## Licensing, Authors, Acknowledgements
mlrose was written by Genevieve Hayes and is distributed under the [3-Clause BSD license](https://github.com/gkhayes/mlrose/blob/master/LICENSE).
You can cite mlrose in research publications and reports as follows:
* Rollings, A. (2020). ***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python, hiive extended remix***. https://github.com/hiive/mlrose. Accessed: *day month year*.
Please also keep the original author's citation:
* Hayes, G. (2019). ***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python***. https://github.com/gkhayes/mlrose. Accessed: *day month year*.
You can cite this fork in a similar way, but please be sure to reference the original work.
Thanks to David S. Park for the MIMIC enhancements (from https://github.com/parkds/mlrose).
BibTeX entry:
```
@misc{Hayes19,
author = {Hayes, G},
title = {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python}},
year = 2019,
howpublished = {\url{https://github.com/gkhayes/mlrose}},
note = {Accessed: day month year}
}
@misc{Rollings20,
author = {Rollings, A.},
title = {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python, hiive extended remix}},
year = 2020,
howpublished = {\url{https://github.com/hiive/mlrose}},
note = {Accessed: day month year}
}
```
%package help
Summary: Development documents and examples for mlrose-hiive
Provides: python3-mlrose-hiive-doc
%description help
# mlrose: Machine Learning, Randomized Optimization and SEarch
mlrose is a Python package for applying some of the most common randomized optimization and search algorithms to a range of different optimization problems, over both discrete- and continuous-valued parameter spaces.
## Project Background
mlrose was initially developed to support students of Georgia Tech's OMSCS/OMSA offering of CS 7641: Machine Learning.
It includes implementations of all randomized optimization algorithms taught in this course, as well as functionality to apply these algorithms to integer-string optimization problems, such as N-Queens and the Knapsack problem; continuous-valued optimization problems, such as the neural network weight problem; and tour optimization problems, such as the Travelling Salesperson problem. It also has the flexibility to solve user-defined optimization problems.
At the time of development, there did not exist a single Python package that collected all of this functionality together in the one location.
## Main Features
#### *Randomized Optimization Algorithms*
- Implementations of: hill climbing, randomized hill climbing, simulated annealing, genetic algorithm and (discrete) MIMIC;
- Solve both maximization and minimization problems;
- Define the algorithm's initial state or start from a random state;
- Define your own simulated annealing decay schedule or use one of three pre-defined, customizable decay schedules: geometric decay, arithmetic decay or exponential decay.
#### *Problem Types*
- Solve discrete-value (bit-string and integer-string), continuous-value and tour optimization (travelling salesperson) problems;
- Define your own fitness function for optimization or use a pre-defined function.
- Pre-defined fitness functions exist for solving the: One Max, Flip Flop, Four Peaks, Six Peaks, Continuous Peaks, Knapsack, Travelling Salesperson, N-Queens and Max-K Color optimization problems.
#### *Machine Learning Weight Optimization*
- Optimize the weights of neural networks, linear regression models and logistic regression models using randomized hill climbing, simulated annealing, the genetic algorithm or gradient descent;
- Supports classification and regression neural networks.
## Installation
mlrose was written in Python 3 and requires NumPy, SciPy and Scikit-Learn (sklearn).
The latest version can be installed using `pip`:
```
pip install mlrose-hiive
```
Once it is installed, simply import it like so:
```python
import mlrose_hiive
```
## Documentation
The official mlrose documentation can be found [here](https://mlrose.readthedocs.io/).
A Jupyter notebook containing the examples used in the documentation is also available [here](https://github.com/gkhayes/mlrose/blob/master/tutorial_examples.ipynb).
## Licensing, Authors, Acknowledgements
mlrose was written by Genevieve Hayes and is distributed under the [3-Clause BSD license](https://github.com/gkhayes/mlrose/blob/master/LICENSE).
You can cite mlrose in research publications and reports as follows:
* Rollings, A. (2020). ***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python, hiive extended remix***. https://github.com/hiive/mlrose. Accessed: *day month year*.
Please also keep the original author's citation:
* Hayes, G. (2019). ***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python***. https://github.com/gkhayes/mlrose. Accessed: *day month year*.
You can cite this fork in a similar way, but please be sure to reference the original work.
Thanks to David S. Park for the MIMIC enhancements (from https://github.com/parkds/mlrose).
BibTeX entry:
```
@misc{Hayes19,
author = {Hayes, G},
title = {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python}},
year = 2019,
howpublished = {\url{https://github.com/gkhayes/mlrose}},
note = {Accessed: day month year}
}
@misc{Rollings20,
author = {Rollings, A.},
title = {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python, hiive extended remix}},
year = 2020,
howpublished = {\url{https://github.com/hiive/mlrose}},
note = {Accessed: day month year}
}
```
%prep
%autosetup -n mlrose_hiive-2.2.4
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-mlrose-hiive -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 2.2.4-1
- Package Spec generated
|
